Clotet Eduard, Palacín Jordi
Robotics Laboratory, Universitat de Lleida, Jaume II, 69, 25001 Lleida, Spain.
Sensors (Basel). 2023 Aug 1;23(15):6841. doi: 10.3390/s23156841.
The Iterative Closest Point (ICP) is a matching technique used to determine the transformation matrix that best minimizes the distance between two point clouds. Although mostly used for 2D and 3D surface reconstruction, this technique is also widely used for mobile robot self-localization by means of matching partial information provided by an onboard LIDAR scanner with a known map of the facility. Once the estimated position of the robot is obtained, the scans gathered by the LIDAR can be analyzed to locate possible obstacles obstructing the planned trajectory of the mobile robot. This work proposes to speed up the obstacle detection process by directly monitoring outliers (discrepant points between the LIDAR scans and the full map) spotted after ICP matching instead of spending time performing an isolated task to re-analyze the LIDAR scans to detect those discrepancies. In this work, a computationally optimized ICP implementation has been adapted to return the list of outliers along with other matching metrics, computed in an optimal way by taking advantage of the parameters already calculated in order to perform the ICP matching. The evaluation of this adapted ICP implementation in a real mobile robot application has shown that the time required to perform self-localization and obstacle detection has been reduced by 36.7% when obstacle detection is performed simultaneously with the ICP matching instead of implementing a redundant procedure for obstacle detection. The adapted ICP implementation is provided in the SLAMICP library.
迭代最近点（ICP）是一种匹配技术，用于确定能使两个点云之间的距离最小化的变换矩阵。尽管该技术主要用于二维和三维表面重建，但它也通过将车载激光雷达扫描仪提供的部分信息与设施的已知地图进行匹配，广泛应用于移动机器人的自我定位。一旦获得机器人的估计位置，就可以分析激光雷达收集的扫描数据，以定位可能阻碍移动机器人规划轨迹的障碍物。这项工作提出通过直接监测在ICP匹配后发现的异常值（激光雷达扫描数据与完整地图之间的差异点）来加速障碍物检测过程，而不是花费时间执行单独的任务来重新分析激光雷达扫描数据以检测这些差异。在这项工作中，一种经过计算优化的ICP实现方法经过调整，以便在利用为执行ICP匹配而已经计算出的参数以最优方式计算其他匹配指标的同时，返回异常值列表。在实际移动机器人应用中对这种经过调整的ICP实现方法进行的评估表明，当与ICP匹配同时执行障碍物检测而不是为障碍物检测实施冗余程序时，执行自我定位和障碍物检测所需的时间减少了36.7%。经过调整的ICP实现在SLAMICP库中提供。
